Threaded learning and signaling in the Qt UI

gui/NeuroUI/netlearner.cpp

@@ -0,0 +1,65 @@
+#include "netlearner.h"
+#include "../../Net.h"
+
+void NetLearner::run()
+{
+    try
+    {
+        Net myNet({2, 3, 1});
+
+        size_t batchSize = 5000;
+        size_t batchIndex = 0;
+        double batchMaxError = 0.0;
+        double batchMeanError = 0.0;
+
+        size_t numIterations = 1000000;
+        for (size_t iteration = 0; iteration < numIterations; ++iteration)
+        {
+            std::vector<double> inputValues =
+            {
+                std::rand() / (double)RAND_MAX,
+                std::rand() / (double)RAND_MAX
+            };
+
+            std::vector<double> targetValues =
+            {
+                (inputValues[0] + inputValues[1]) / 2.0
+            };
+
+            myNet.feedForward(inputValues);
+
+            std::vector<double> outputValues = myNet.getOutput();
+
+            double error = outputValues[0] - targetValues[0];
+
+            batchMeanError += error;
+            batchMaxError = std::max<double>(batchMaxError, error);
+
+            if (batchIndex++ == batchSize)
+            {
+                QString logString;
+
+                logString.append("Batch error (");
+                logString.append(QString::number(batchSize));
+                logString.append(" iterations, max/mean): ");
+                logString.append(QString::number(std::abs(batchMaxError)));
+                logString.append(" / ");
+                logString.append(QString::number(std::abs(batchMeanError / batchSize)));
+
+                emit logMessage(logString);
+
+                batchIndex = 0;
+                batchMaxError = 0.0;
+                batchMeanError = 0.0;
+            }
+
+            myNet.backProp(targetValues);
+        }
+    }
+    catch (std::exception &ex)
+    {
+        QString logString("Error: ");
+        logString.append(ex.what());
+        emit logMessage(logString);
+    }
+}